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首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Multiple Kernel SVR Based on the MRE for Remote Sensing Water Depth Fusion Detection
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Multiple Kernel SVR Based on the MRE for Remote Sensing Water Depth Fusion Detection

机译：基于MRE的多核SVR遥感水深融合检测

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摘要

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2%、1.9%、3.4%、1.8%, and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.
机译：遥感是沿海浅水区和礁石中水深检测的重要手段。支持向量回归（SVR）是一种广泛用于数据回归的机器学习方法。本文将SVR用于遥感多光谱测深。针对单核SVR方法在浅水深度反演中存在较大误差的问题，采用单核SVR方法（多核SVR融合）获取不同水深的平均相对误差（MRE）作为决策融合因子。提出了基于MRE的方法。并以中国西沙群岛北岛为实验区，进行了单核SVR方法与传统多波段测深方法的对比实验。结果表明：1）在0至25米范围内，多核SVR融合方法的平均绝对误差（MAE）为1.5m，MRE为13.2％; 2）与4个单核SVR方法相比，融合方法的MRE降低了1.2％，1.9％，3.4％，1.8％，与传统的多频段方法相比，MRE降低了1.9％; 3）在0-5m深度段，与单核方法和多频带方法相比，融合方法的MRE降低了13.5％至44.4％，并且点的分布相对于y = x更加集中。

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